The popularity of t-SNE attracted many researchers who proposed several variations and extensions of the algorithm. Recently, Cheng et al. (2020) proposed St-SNE [1]}, a supervised extension of t-SNE. Similarly to our proposal, St-SNE considers the labelling information as an additional data-view. In contrast to S-multi-SNE, the unlabelled samples are classified differently. They proposed three strategies for classification, one of which uses Neural Networks (nSt-SNE) and it is similar to the parametric t-SNE [2]}. Another strategy (dSt-SNE) implements St-SNE twice to predict the classes of unlabelled samples. The latter strategy was used to compare a single-view approach (St-SNE) against a multi-view approach (S-multi-SNE) and to highlight the benefits of incorporating multiple data-views in the analysis.
